POS1514 DIFFERENTIALLY EXPRESSED GENES OF PATIENTS WITH LUPUS NEPHRITIS THROUGH INTEGRATIVE BIOINFORMATICS
Authors: R. Martinez, C. Elizondo Solis, A. Montoya Rosales, M. Salinas Carmona, N. Macias-Segura
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Systemic Lupus Erythematosus (SLE) is an autoimmune chronic disease characterized clinically by periods of flares and remission. Although its pathophysiology has not yet been well understood, it is well documented that SLE courses with systemic inflammation, as well as affection to specific organs [1]. Lupus nephritis (LN) is the most common complication and cause of death appearing in SLE patients, often leading to end-stage renal disease [2]. A type I interferon (IFN) signature is present in SLE, however, there is no consensus on which genes are expressed in LN. We performed a metanalysis through integrative bioinformatics to identify differentially expressed genes (DEGs) in patients with LN. <h3>Objectives</h3> We aimed to identify and assess overlapped DEGs of patients with LN through integrative bioinformatics and functional enrichment analysis. <h3>Methods</h3> We designed a search strategy in the Gene Expression Omnibus platform to identify datasets of expression profiling by array of kidney samples of patients with LN. The inclusion criteria were: 1) Presence of healthy controls in the datasets, and 2) Analysis of data with GEO2R. The exclusion criteria were: 1) Incomplete information in the datasets, 2) Failure to identify cases and controls, or 3) Gene expression through RNAseq. DEGs were filtered when <i>p</i> &lt; 0.05 and logfold change &gt; 2 or &lt; -2. We assessed the DEGs with David database and performed a prediction analysis with GeneMania. Protein-protein interactions analysis was realized with String through Cytoscape platform, and iRegulon was used to predict a possible regulatory target of the cluster. <h3>Results</h3> 3 datasets fulfilled the inclusion criteria and 9 DEGs were identified. Prediction analysis yielded 23 co-expressed genes. David database recognized the principal biological processes involving those genes (Table 1). PPI analysis recognized the top 10 genes with the strongest interactions, and proposed STAT1 as the regulatory target (Figure 1). <h3>Conclusion</h3> We aimed to identify and assess DEGs of patients with LN and recognize the ones with the strongest interactions. Our results suggest that type I IFN is crucial on the pathophysiology of LN, and STAT1 can serve as a possible therapeutic target for treating LN. We propose further <i>in vivo</i> studies to validate these results, given that they can serve as diagnostic biomarkers and even possible therapeutic targets for LN treatment. <h3>References</h3> [1] Zucchi, Dina et al. "One year in review 2022: systemic lupus erythematosus." Clinical and experimental rheumatology vol. 40,1 (2022): 4-14. doi:10.55563/clinexprheumatol/nolysy [2] Gasparotto, Michela et al. "Lupus nephritis: clinical presentations and outcomes in the 21st century." Rheumatology (Oxford, England) vol. 59,Suppl5 (2020): v39-v51. doi:10.1093/rheumatology/keaa381 [3] Der, Evan et al. "Tubular cell and keratinocyte single-cell transcriptomics applied to lupus nephritis reveal type I IFN and fibrosis relevant pathways." Nature immunology vol. 20,7 (2019): 915-927. doi:10.1038/s41590-019-0386-1 <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> None Declared.